ML System Design interview questions

Showing 151–180 of 332 results

• Design a Credit Scoring PlatformMedium

  This question assesses the ability to design an end-to-end machine learning system for real-time credit risk assessment, focusing on data pipelines, model inference, and regulatory compliance.

  KikoffSoftware EngineerML System DesignOnsite
• Design a cloud AI inference platformHard

  This question evaluates competency in ML system design and operational engineering, covering model packaging and versioning, hardware selection (CPU vs GPU), autoscaling, request routing, multi-tenant isolation, SLO-driven observability, cost controls, and failure handling within a cloud inference platform.

  LambdaSoftware EngineerML System DesignHR Screen
• Design a DNA-sequence optimization loopMedium

  This problem tests your ability to design a closed-loop active learning system for protein or DNA sequence engineering, focusing on the iterative cycle of sequence generation, uncertainty estimation, and experimental validation.

  LilaMachine Learning EngineerML System DesignOnsite
• Design LinkedIn Learning course recommendationsMedium

  The question evaluates proficiency in end-to-end machine learning system design, covering feature engineering from job post text, candidate generation and ranking, model training and labeling, evaluation metrics, and production concerns such as cold start, bias, and feedback loops.

  LinkedInMachine Learning EngineerML System DesignOnsite
• Design a Model That Predicts Whether a Member Clicks a Displayed JobHard

  An ML system design question: predict whether a member will click a job displayed to them, using member features, job content and interaction history. It tests defining the supervision signal and training data, choosing a loss, preventing data leakage, building member and job embeddings, and laying out the training and serving components.

  LinkedInMachine Learning EngineerML System DesignTechnical Screen
• Design a Personalized Feed and Compare Ranking ObjectivesMedium

  Design an end-to-end personalized professional feed system and compare pointwise, pairwise, and listwise learning-to-rank formulations across offline training, online serving, and business metrics.

  LinkedInMachine Learning EngineerML System DesignTechnical Screen
• Design a Skills inference systemMedium

  This question assesses the ability to build an end-to-end ML pipeline for extracting and normalizing professional skills from unstructured user-generated text, such as job descriptions or member profiles.

  LinkedInMachine Learning EngineerML System DesignOnsite
• Design a system for LinkedIn SkillsMedium

  This question evaluates a machine learning engineer's ability to design an end-to-end ML system for skill inference and recommendation on a professional networking platform, testing competencies in data sourcing and labeling, feature engineering, model selection (including LLM integration), online serving, monitoring, and quality control.

  LinkedInMachine Learning EngineerML System DesignOnsite
• Design an ML Forecasting and Pacing System for Ad BiddingMedium

  Design an ad forecasting and budget pacing architecture that combines predictive ML with deterministic budget guardrails to optimize campaign delivery across non-stationary traffic.

  LinkedInMachine Learning EngineerML System DesignTechnical Screen
• Manage Prompt and Model Behavior in ProductionHard

  This question tests the ability to design an end-to-end configuration management and observability system for LLM applications that ensures reproducibility, safety, and rapid recovery from performance drift.

  Luma AISoftware EngineerML System DesignTechnical Screen
• Build a Candidate Search SystemMedium

  Design an end-to-end talent search and matching engine that combines semantic vector retrieval with structured filtering to handle unstructured resumes, ambiguous queries, and strict hiring constraints.

  MercorMachine Learning EngineerML System DesignOnsite
• Build an Ollama Eval HarnessMedium

  This question evaluates a candidate's ability to design and implement a lightweight evaluation harness for a locally hosted language model, testing competencies in ML system design, service integration, metrics instrumentation, reliability engineering (timeouts, retries), concurrency control, configuration management, and extensibility.

  MercorMachine Learning EngineerML System DesignTechnical Screen
• Design Candidate Search And LLM Evaluation TasksMedium

  Prepare for an MLE system design interview that combines candidate search, ranking, comparator reliability, and language-model fundamentals. The prompt covers retrieval, ranking feedback loops, top-k reasoning, transformer concepts, and decoding trade-offs without requiring proprietary hiring data.

  MercorMachine Learning EngineerML System DesignOnsite
• Design Work Orchestration for Machine-Learning Data PipelinesMedium

  Design a resilient, multi-tenant orchestration engine tailored for machine learning pipelines that manages versioned DAGs, heterogeneous compute routing, data quality gates, and atomic dataset publication.

  MercorMachine Learning EngineerML System DesignOnsite
• Architect an asynchronous RL post-training systemHard

  This question evaluates a candidate's ability to design an asynchronous reinforcement-learning post-training system for a production chat LLM, testing competencies in ML system architecture, distributed training and serving separation, streaming data engineering, reward modeling and credit assignment, safety/compliance, and deployment/operations.

  MetaMachine Learning EngineerML System DesignOnsite
• Build a Mistral-powered RAG agentHard

  This question evaluates a candidate's ability to design and implement a retrieval-augmented generation (RAG) system using a large language model API, including document ingestion, chunking, vector indexing and retrieval, streaming completions, service interfaces (CLI and HTTP), and resilience features like retries, persistence, and structured error handling. Commonly asked in the ML System Design domain to assess practical engineering and architectural reasoning, it tests API integration, production-readiness (configuration, error handling, persistence, and testing) and emphasizes practical application with conceptual understanding of retrieval trade-offs.

  MetaSoftware EngineerML System DesignOnsite
• Design Nearby and Notification RankingHard

  This question evaluates competency in designing large-scale machine learning systems for real-time recommendations and notification ranking, covering candidate generation, ranking, feature engineering, online serving, latency management, and multi-objective trade-offs; it is commonly asked to assess the ability to translate product objectives into system architecture and measurable evaluation metrics while addressing freshness, cold-start, moving users, and notification fatigue. The domain is ML system design—recommender systems, ranking, and online serving—and the level spans both high-level conceptual architecture and practical application concerns such as serving latency, data freshness, training labels, and evaluation.

  MetaMachine Learning EngineerML System DesignOnsite
• Design Place Recommendation SystemMedium

  This question evaluates machine learning system design skills specific to recommender systems, including competencies in feature engineering, candidate generation and ranking, data engineering, online serving, latency and real-time context handling, evaluation metrics, and fairness/bias mitigation.

  MetaMachine Learning EngineerML System DesignOnsite
• Design a Code Review AgentMedium

  This question evaluates a candidate's ability to design ML-powered developer tooling, covering competencies in LLM orchestration, deterministic tool integration, repository context management, code-analysis heuristics, safety and validation strategies, and inference cost estimation.

  MetaSoftware EngineerML System DesignOnsite
• Design a Large-Scale Personalized Recommendation System End to EndMedium

  An ML system design question asking you to design a large-scale personalized recommendation system end to end, from candidate retrieval and multi-stage ranking to training data, evaluation, serving and monitoring. It tests problem framing, latency budgeting, cold start, feedback loops and online experimentation.

  MetaMachine Learning EngineerML System DesignOnsite
• Design a Real-Time Question-Answering Chatbot: Training Data, RL Tasks and RewardsMedium

  An AI design question about a chatbot that answers user questions in real time. It tests the low-latency serving and retrieval path, training data collection for fine-tuning and preference learning, reinforcement-learning task and reward design, and the ability to name reward weaknesses and mitigate them.

  MetaMachine Learning EngineerML System DesignTechnical Screen
• Design a Short-Video Recommendation SystemMedium

  This question tests your ability to design a multi-stage recommendation pipeline for a high-traffic short-video platform, focusing on candidate retrieval, ranking models, and system scalability.

  MetaMachine Learning EngineerML System DesignTechnical Screen
• Design a newsfeed dislike modelMedium

  This problem tests the ability to design an end-to-end ML system for predicting user dissatisfaction, focusing on label definition, feature engineering, and the integration of negative signals into a ranking pipeline.

  MetaMachine Learning EngineerML System DesignOnsite
• Design a scalable MoE pretraining pipelineHard

  This question evaluates expertise in large-scale ML system design and distributed training for Mixture-of-Experts (MoE) models, covering competencies in model architecture, parallelism and communication strategies, memory and optimizer sharding, data curation and tokenization, monitoring, checkpointing, and fault tolerance; it is categorized under ML System Design and targets practical application with systems-level architectural reasoning rather than purely theoretical concepts. It is commonly asked to evaluate the ability to balance throughput, stability, and cost when scaling pretraining to hundreds of GPUs, reason about routing and capacity trade-offs, and design production-grade pipelines that can extend to larger clusters.

  MetaMachine Learning EngineerML System DesignOnsite
• Design a weapon-sale ad detection systemMedium

  This question tests the ability to architect an end-to-end multimodal classification pipeline for detecting policy-violating content at scale while balancing precision, recall, and operational latency.

  MetaMachine Learning EngineerML System DesignOnsite
• Design an Automated Ticket Investigation AgentHard

  This question evaluates a candidate's ability to design AI-enabled agentic systems for automated ticket investigation, covering competencies in ticket understanding, classification and prioritization, multi-source evidence retrieval (logs, metrics, traces, documentation), root-cause diagnosis, remediation orchestration, and human-in-the-loop safety controls. It is commonly asked in the ML system design domain to assess architectural and integration thinking, data retrieval and evaluation strategies, production monitoring and safety considerations, and tests both high-level conceptual architecture and practical application-level implementation concerns.

  MetaSoftware EngineerML System DesignTechnical Screen
• Design an LLM-Based Coding AssistantHard

  Design an end-to-end LLM-powered coding assistant supporting inline code completion and repository-level chat under strict latency constraints.

  MetaMachine Learning EngineerML System DesignOnsite
• Design an LLM-Based Conversational Assistant (Chatbot)Hard

  Design an end-to-end conversational AI assistant powered by large language models, covering post-training alignment, retrieval/tool orchestration, low-latency serving, and safety guardrails at consumer scale.

  MetaMachine Learning EngineerML System DesignOnsite
• Design an image copyright-violation detection systemMedium

  This problem evaluates your ability to design a scalable, end-to-end computer vision pipeline capable of identifying copyrighted content within user-uploaded images, including scenarios involving cropping, filters, and collages.

  MetaMachine Learning EngineerML System DesignOnsite
• Design comment ranking for a news feedMedium

  This question tests the ability to design an end-to-end machine learning system for ranking comments, covering feature engineering, model architecture, and the trade-offs between engagement, quality, and latency.

  MetaMachine Learning EngineerML System DesignOnsite